In recent years, texture analysis in which general image pattern or image property is quantified as a function of spatial variation of pixel values in terms of image texture such as roughness, smoothness, gloss, and irregularity has been studied.
Texture analysis is one of representative analysis methods in characterization analysis, and characterization analysis includes image analysis such as histogram analysis and spectrum analysis.
Applying texture analysis to a medical image, e.g., a region of interest ROI in a tumor or an organ has been studied. Specifically, it is expected that texture analysis is effectively used for obtaining medical prediction in a target region of a medical image as well as determining whether the target region is benign or malignant. Additionally, it is known that accuracy of texture analysis is influenced when there is difference in feature between a region of interest ROI and an actual target region of a medical image to which texture analysis is applied.
However, when a user manually selects a region of interest ROI in a certain medical image, it is difficult to select this region of interest ROI in a perfectly appropriate manner. Even if a region of interest ROI is automatically selected by image analysis, it is also difficult to select this region of interest ROI in a perfectly appropriate manner because a certain degree of error may be included in the image analysis.